# 2006 TEXAQS/GoMACCS Comprehensive Meteorological Analysis of TexAQS II data

Combining Climate Change and Air Quality Research August 2007 -108 -106 -104 -102 -100 -98 -96 -94 -92 Longitude 26 28 30 32 34 36Latitude

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TexAQS-II Enhancements

### Introduction NOAA ESRL has undertaken a comprehensive analys is of meteorological data collected during the TexAQS II experiment. This report describes th e results of that analys is to date. The report is organized according to the questions presented by TCEQ. Initial findings were presented at the TEXAQ S II / GoMACCS Data Analysis Workshop that was held in Austin on May 29 – June 1, 2007. Subsequent analysis has added to our understanding. The findings are presented here in the form of brief summar ies. We also discuss planned further analysis. Data from many sources has been used in the following analyses. Most prominent among these are the rawinsondes launched from the NOAA Res earch Vessel Ronald H. Brown, the radar wind profilers deployed by TCEQ and NOAA, the High Resolu tion Doppler Lidar on Brown, and the TOPAZ airborne lidar.

I. Data review and conceptual model Background: Nielsen-Gammon (2005) and Bant a et al. (2005) have presente d conceptual models for the highest ozone events in the Houston area. Th ese emphasize the role of the coastal oscillation (bay breeze and sea breeze) in producing periods of very low wind speeds, which allow air parcels to accumulate emissions for a long time or multiple periods of time. These models remain valid for those events. However, it has been known since the TexAQS 2000 experiment that very low winds are not necessary for concen trations of ozone exceeding regulatory standards to be formed in the Houston area. Analys is of TexAQS II data has confirmed this.

Results: The overall relationship between peak O 3 concentrations and wind speed is shown in Fig. 1, which indicates the expected strong negative co rrelation between maximum in-network ozone enhancements above background and wind speed in the Houston area. Stronger winds produce greater dilution of the pollution emissions, and thus lower concentrations of pollutants. The blue points on Fig. 1 show the airborne-lidar -determined peak ozone concentrations vs. 10- hr trajectory displacements, a surrogate for mean wind speed. Trajectories were calculated for heights in the middle of the boundary layer from the profiler network. The concentrations again decrease with increasing speed, but are everyw here larger than the surface measurements. Reasons for this trend include that the airborne data represent conditions aloft not subject to surface processes including removal, that the airbor ne lidar data represent averaging over shorter intervals, and that on days w ith stronger winds, plumes are na rrower and harder to sample by fixed networks and also high c oncentrations may be blown out of the surface network before being sampled.Figure 1: Houston ozone enhancement (peak ozone values in urban plume minus background values) plotted vs. displacement of 10-hr trajectories, representing the vector-mean wind for the period, starting at Houston at 8:00 a.m. CST. Red symbols indicate data from surface measurement network (1-hr average), and blue sym bols indicate data from airborne ozone lidar. “Add on” represents peak ozone concentrations observed minus background concentrations. This last point was expected to be a greater source of discre pancy between airborne and groundnetwork sampling, because the aircraft samples the pollution plume wherever it is, even though the maximum values may be occurring beyond the surface measurement network before the photochemical reactions are complete. Indeed, i ndividual days have been identified with large discrepancies. In the overall sampling scheme , however, Fig. 1 shows that light winds (small 10- hr displacements) were associated with large O 3 enhancements by the Houston area and stronger wind speeds were associated with smaller enhanc ements. However, several stronger-wind days have been identified when the total O 3 concentrations (background plus add-on) exceeded the 1- hr standard. These days, most often associated with high background values, are being studied further to determine all processes re sponsible for the high concentrations. Interpretation and conclusions: Light winds and the coastal oscill ation contribute to the highest ozone levels in Houston, but emissions are sufficient to produce strong ozone plumes downwind even on days with moderate winds. See also RSST Question B report section B2.

II. Other sources of data Satellite images were archived by Owen Cooper at http://www.esrl.noaa.gov/cs d/metproducts/texaqs/ Jerome Brioude has created a satellite cloud prod uct combining infrared and visible imagery at 4-km resolution every daytime hour from the GOES geostationary satellites. A cloud mask for each hour is calculated by selecting the darkest pixels over a 30 day time period. Then, pixels having a brightness temperature higher than 17K compared to the darkest pixel are defined as cloudy pixels. The cloud-top temperature is re trieved from the cloud mask applied on the infrared imagery. Then, cloud top altitude is estimated by cross referencing the cloud-top temperature with temperature fields from the 3-hourly ECMWF analyses (0.36x0.36 horizontal resolution), linearly interpolated in space and time . This dataset has a high temporal resolution(one hour) and relatively high geogr aphical resolution (4km) to be used to study processes such as the sea breeze, the evolution of the daytime bounda ry, as well as the diurnal variation of low, mid and high level cloud acr oss the Gulf of Mexico. Real-time data from the radar wind profilers operated by NOAA ESRL PSD are available at http://www.etl.noaa.gov/et7/data/archive/Unedite dNonActive. Quality-controlled data are available on request. Quality-controlled data and products including winds and mixing depth from the NOAA ESRL CSD profiler at Moody are available from Wa yne Angevine (Wayne.M.Angevine@noaa.gov).

III. Were lower maximum ozone concentrat ions observed during TexAQS II meteorologically driven? Background: Although concentrations observed by surface monito rs were lower in 2006 than in 2000, it is unclear whether concentrations were lower in ge neral. Preliminary analysis of the airborne ozone lidar data indicates that peak concen trations observed in 2006 approached values observed during the 2000 study. A comprehensiv e analysis will more precisely assess similarities and diffences between the two year s. Wind patterns and mixing heights in the two years were also somewhat diffe rent, and the implications of this are also under analysis. Interpretation and conclusions: Further analysis is needed to determine whether there were significant differences in ozone concentrations between 2000 and 2006, and if so, whet her those differences can be attributed to meteorology.

IV. What was the role of the coastal oscillatio n in ozone and ozone p recursor accumulation and advection? Background: The prevailing conceptual model for the highest ozone concentrations in Houston involves stagnant or recirculating winds which allow for prolonged or multiple exposure of an air mass to emissions. The winds stagnate or reverse beca use of land-water temperature contrasts producing phenomena called land, bay, gulf, or sea breezes. B ecause Houston is almost at the critical 30 degree latitude, these circulations have different characteristics than at higher latitudes, particularly a larger horizontal scale. The irregular shape of the coastline adds complexity. These phenomena are lumped together under the general term "coastal oscillation."

Results: As discussed above, we continue to think that the coastal oscillation is associated with the strongest ozone events. For example, on 1 September 2006, instruments on the Ron Brown detected ozone mixing ratios over 180 ppb in Ga lveston Bay (figure 2). The high ozone was measured while the winds at the ship were from the east. Analysis shows that the air traversed the ship channel, went out over the Bay, and returned on the recirculating flow. Interpretation and conclusions: The coastal oscillation concept is an important tool for understandi ng the highest ozone events in Houston.Figure 2: Ozone mixing ratio and wind dir ection from the ship on 1 September.V. What was the role of the heat island e ffect over city, and over industrial areas? Background: Urban areas become hotter during the day and re main hotter at night than surrounding rural areas. This may affect wind patterns and thus pollutant transport, for example by strengthening or distorting land – sea breeze patterns. It also affects biogenic VOC em issions, and may affect anthropogenic fugitive VOC emissions.

Results: Both Dallas and Houston have comprehensive networks of surface meteorology and chemistry sensors. The similarities of the networks and la ck of terrain in Dallas and Houston allow for the comparison of their urban heat islands (UHI). The Dallas UHI, unperturbed by thermal flows driven by the land/sea temperature difference, is a well-defined phenomenon over the summers of 2000-2006 (Fig. 3a). Including all weat her conditions, the average nighttime T urban – T rural temperature difference was betw een 1.5o and 2.0o C and the aver age daytime difference was ~ 1.0o C. Analysis of Houston temperature data, ho wever, revealed a different picture due to the bay and gulf breezes (Fig. 3b). While the Hous ton UHI was a distinct phenomenon, even when including all weather conditions, the bay or gul f breeze modified the Houston UHI by cooling the city. Average nighttime T urban – T rural temperature differences in Houston were between 1.75o and 2.75o C. However, during the day, the rural areas to the nor th and west of the city were often warmer than the downtown area during af ternoon hours as a result of the sea breeze. Averaging the Houston T urban – T rural temperature differences over the summers of 2000-2006 indicated a very small urban-rural temperatur e difference between 1400 to 1600 LST. In some individual years, such as 2000, 2003, 2005 and 2006, the urban areas were actually cooler than the rural areas, on average, in the mid-afternoon. These years had more bay breeze/gulf breeze activity to cool the urban area.

Figure 3: Turban – Trural for each of the 7 summers analyzed, and the average for all summers. “Summer” includes all days from June 1 – September 30. a) Dallas. b) Houston. Interpretation and conclusions: Houston has a distinct urban heat island signature , but it is different from the more classical situation exemplified by Dallas because of the coas tal oscillation. Implications of this for ozone and aerosol transport, as distin ct from the coastal oscillation winds, remain to be explored.VI. What was the role of low mixi ng depths over Galveston Bay? Background: Mixing depth is a first-order cont rol on pollutant concentrations, defining the vertical dimension of the volume through which emissions and products are diluted. Mixing depths over Galveston Bay have been thought to be generally lo wer than over surrounding land at midday (and probably higher at night), but measurements have been scarce.

Results: The lidars on Brown, and rawinsondes launche d from the ship, provide much improved measurements of mixing depths over the Bay during the 2006 study. In addition, surface heat, momentum, and moisture fluxes were measured on the ship, providing information about the local forcing contributing to the mixing depth. Figure 4 shows mixing depths determined from the lidar measurements for the Gulf, Bay, and areas surrounded by land. Average mixing depths over the Gulf showed little diurnal cycle. Over the Bay, there was some sign of diurnal variation, an unknown fraction of whic h is due to measuring air that has recently been influenced by land. However, the land influence is clearly less than at the locations surrounded by land. We can conclude that mixing depths over the Ba y are generally lower th an over land. Heat flux at the surface (figure 5) supports this, having valu es that are generally small but positive. As a specific example, figure 6 shows a sounding from the ship near the time of the peak ozone measurement on 1 September. The sounding locati on was in the Bay (figur e 2) and the lowest layer wind was from the East, so the air in that layer had been over the bay for some time. A single mixing depth is difficult to define from this sounding, since there are multiple weakly defined layers, but the mixing de pth is definitely below 400 m.

Figure 4: Average mixing depths from the HRDL lidar measurements for locations in the Gulf, Bay, and surrounded by land. Interpretation and conclusions:We now have good evidence that mixing depths ov er the Bay during the day are lower than over the surrounding land. In the ideal scenario of hi ghest ozone formation, air passes over the ship channel area, picking up large con centrations of precursor emissi ons, and then stagnates over the Bay while photochemistry proceeds. Moderate mixing depth over the Bay keeps the concentrations relatively strong by limiting vertical dilution. This is the scenario that produced the strong ozone measured at the ship on 1 Se ptember 2006, as well as at surface monitors. When the continuing coastal oscillation moves this air back over the urban area, it picks up more emissions. Depending on the time of day, it may also be mixed to greater depth as it moves inland. Thus the influence of the lower mixi ng depth over the Bay may be limited to areas relatively near shore. It should also be noted that there were days when the lower mixed layer over the Bay was cleaner than air in the lower layer in surrounding areas.

Figure 5: All 10-minute heat flux measurements from the ship when it was in Galveston Bay, plotted versus time of day (left) and wind direction (right).Figure 6: Rawinsonde sounding from Brown launc hed in the Bay at 1752 UTC 1 September.

VII. What was the relationship between mixing depth and land cover?

Background: We generally expect that land use or land cover types that are wetter will lead to lower mixing depths than drier areas. Climat ologies and vegetation indicate mo ister conditions to the east of Houston and nearer to the coast, and more arid conditions to the west and inland. However, there are many complicating factors, including ant ecedent rainfall and current cloud cover. This question is also related to the urban height island (section V).

Results: Mixing depth and aerosol layer de pth measurements from wind prof ilers and airborne lidar give a general picture of the regional distribution of mixing depth. Airborne lid ar flights revealed a significantly deeper mixed layer over the arid re gions to the west, which is consistent with mixing-depth data from the profiler array, which indicated increases in peak afternoon mixing depths with distance from the coast. For exampl e, figure 7 shows the sta tistical distributions of midday mixing depth from wind profilers at Moo dy and LaPorte. The distribution at Moody issharply peaked near 1.5 km, while LaPorte has many lower heights and a broader distribution. Some of this variation is due to the coastal oscillation.

Figure 7: Distribution of boundary layer heig hts (mixing heights) for Moody (upper) and LaPorte (lower) between 1 August and 15 September 2006. The airborne lidar data are being analyzed, and will provide a regional picture of mixing height on flight days. An example is shown in fi gure 8 for 16 August 2006. It shows the expected pattern of higher mixing heights to the north west and lower heights near the coast.Figure 8: Mixed layer height (aerosol layer height) from the TOPAZ airborne lidar on 16 August 2006. Interpretation and conclusions: There are definite patterns of differing mixing height in the region, but they are not simply attributable to differences in land cover. Futu re analyses and modeling studies should shed more light on this question.

VIII. Trajectories Background: Trajectories are a commonly used tool to study the sources of polluted air masses. There are significant uncertainties associated with any calc ulated trajectory. A part icle dispersion model provides a better picture of the uncertainties. We recommend that all available sources be consulted when attempting to analyze a particular case.Results: Trajectories may be created from wind profiler observations at http://www.etl.noaa.gov/programs/2006/texaqs/tra j. An example is shown in figure 9.

Figure 9: Trajectory plot from the wind profiler trajectory tool for 1 September 2006. Twelvehour backward trajectories at four height bands are shown. Particle dispersion model results for the Te xAQS II mobile platforms are available at http://zardoz.nilu.no/~andreas/TEXAQS (these are a product of Andreas Stohl). Custom plots can be made interactively at http:/www.esrl.noaa.gov/csd/metpro ducts/flexpart/flexpart_interac tive/flexpart_custom.html. An example is shown in figure 10.Figure 10: North American anthropogenic CO in the column for 1800 UTC 1 September 2006, calculated from the Flexpart model with GFS meteorology.

IX. What was the role of meteorology in so -called transient high ozone events? Background: Rapid increases of ozone at surface monitoring s ites have been termed "transient high ozone events." There has been some suspicion that these events were due to unknown or unusual chemistry.

Results: Airborne mapping of ozone plumes shows that, n ear the sources, plumes can be rather sharpedged. Shifts in wind direction due to the coasta l oscillation or larger-s cale dynamics can advect these plumes over a monitor, leading to rapid increases in ozone at that site.Interpretation and conclusions: Meteorology is a sufficient explanation fo r so-called transient high ozone events.